Cancer cells often rely on glycolysis to obtain energy and support anabolic growth. Several studies showed that glycolytic cells are susceptible to cell death when subjected to low glucose availability or to lack of glucose. However, some cancer cells, including glycolytic ones, can efficiently acquire higher tolerance to glucose depletion, leading to their survival and aggressiveness. Although increased resistance to glucose starvation has been shown to be a consequence of signaling pathways and compensatory metabolic routes activation, the full repertoire of the underlying molecular alterations remain elusive. Using omics and computational analyses, we found that cyclic adenosine monophosphate-Protein Kinase A (cAMP-PKA) axis activation is fundamental for cancer cell resistance to glucose starvation and anoikis. Notably, here we show that such a PKA-dependent survival is mediated by parallel activation of autophagy and glutamine utilization that in concert concur to attenuate the endoplasmic reticulum (ER) stress and to sustain cell anabolism. Indeed, the inhibition of PKA-mediated autophagy or glutamine metabolism increased the level of cell death, suggesting that the induction of autophagy and metabolic rewiring by PKA is important for cancer cellular survival under glucose starvation. Importantly, both processes actively participate to cancer cell survival mediated by suspension-activated PKA as well. In addition we identify also a PKA/Src mechanism capable to protect cancer cells from anoikis. Our results reveal for the first time the role of the versatile PKA in cancer cells survival under chronic glucose starvation and anoikis and may be a novel potential target for cancer treatment.

www.caspur.it/ASPicDB

Alternative splicing is emerging as a major mechanism for the expansion of the transcriptome and proteome diversity, particularly in human and other vertebrates. However, the proportion of alternative transcripts and proteins actually endowed with functional activity is currently highly debated. We present here a new release of ASPicDB which now provides a unique annotation resource of human protein variants generated by alternative splicing. A total of 256 939 protein variants from 17 191 multi-exon genes have been extensively annotated through state of the art machine learning tools providing information of the protein type (globular and transmembrane), localization, presence of PFAM domains, signal peptides, GPI-anchor propeptides, transmembrane and coiled-coil segments. Furthermore, full-length variants can be now specifically selected based on the annotation of CAGE-tags and polyA signal and/or polyA sites, marking transcription initiation and termination sites, respectively. The retrieval can be carried out at gene, transcript, exon, protein or splice site level allowing the selection of data sets fulfilling one or more features settled by the user. The retrieval interface also enables the selection of protein variants showing specific differences in the annotated features. ASPicDB is available at http://www.caspur.it/ASPicDB/.

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

No abstract